Third International Seminar on Artificial Intelligence, Networking, and Information Technology最新文献_第5页

Research on multi-channel interaction optimization design of household intelligent clothes hanger based on Kano-QFD 基于Kano-QFD的家用智能衣架多通道交互优化设计研究

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667199

Xiyu Gu, Huaming Peng

{"title":"Research on multi-channel interaction optimization design of household intelligent clothes hanger based on Kano-QFD","authors":"Xiyu Gu, Huaming Peng","doi":"10.1117/12.2667199","DOIUrl":"https://doi.org/10.1117/12.2667199","url":null,"abstract":"To optimize the multi-channel interactive experience of the household intelligent clothes hangers, the Kano model is used to qualitatively analyze 15 elements from the two demand levels of \"practicality and ease of use\", and the SII-DDI matrix analysis diagram is made. The importance ranking of 18 design requirements in two dimensions of \"function and interaction\" is constructed by the QFD method. The results show that: (1) \"convenient storage\" and \"knowing process\" are the requirements of household intelligent clothes hanger users \"Remote control\", \"aseptic\", \"quick drying\" and \"ensuring night safety\" are expected requirements. (2) The absolute importance of \"client remote control\", \"human sense recognition\" and \"visual display operation\" are 447, 428 and 428 respectively, which are three interaction modes with high user satisfaction. (3) The design points of \"folding storage\" and \"intelligently matching the drying temperature to ensure the comfort of special fabrics\" are the most important for the product function upgrading of the existing household intelligent clothes hanger Market.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123327050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video-text cross-modal retrieval algorithm based on multiple coding 基于多重编码的视频文本跨模态检索算法

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667669

Yufan Xu

引用次数: 0

Event entity alignment for multi-source encyclopedia knowledge bases with the similarity of event element sets 基于事件元素集相似性的多源百科知识库事件实体对齐

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667213

Yiling Deng, Luo Chen, Ye Wu, Y. Mai, W. Xiong

{"title":"Event entity alignment for multi-source encyclopedia knowledge bases with the similarity of event element sets","authors":"Yiling Deng, Luo Chen, Ye Wu, Y. Mai, W. Xiong","doi":"10.1117/12.2667213","DOIUrl":"https://doi.org/10.1117/12.2667213","url":null,"abstract":"The key to constructing an event knowledge graph is to acquire event knowledge. At present, the method of event extraction from text is not accurate enough, while the event information obtained through encyclopedia knowledge bases has the advantages of high accuracy, good structure and rich multimedia resources. However, acquiring event entities from a single encyclopedia knowledge base has the problem of missing information, so the fusion technology for multisource encyclopedia knowledge bases is needed urgently, in which entity alignment is the core technology. Aiming at the shortcomings of current alignment methods focusing on static entity in event entity alignment, we propose an event entity alignment method based on event elements, which calculates entity similarity according to multiple event elements. In contrast to the algorithm based on latent Dirichlet allocation (LDA) model and the method based on representation learning using BERT, the proposed method provides a significant performance improvement in event entity alignment. Especially, the method optimizes the threshold setting so that it enhances the ability to identify the presence of aligned entities.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"12587 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129967475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incidence trend and prediction of hepatitis C based on stacked LSTM 基于堆叠LSTM的丙型肝炎发病趋势及预测

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667723

Jialong Zhang, Guo Wang, Hongyan Liu, Peng Liu, Xiaxu He

{"title":"Incidence trend and prediction of hepatitis C based on stacked LSTM","authors":"Jialong Zhang, Guo Wang, Hongyan Liu, Peng Liu, Xiaxu He","doi":"10.1117/12.2667723","DOIUrl":"https://doi.org/10.1117/12.2667723","url":null,"abstract":"Objective To explore the prediction of hepatitis C incidence by stacked LSTM model. Methods Aiming at the incidence trend and the number of cases of hepatitis C in China from 2007 to 2017, the ARIMA, NNAR, SVR and stacked LSTM were used to train them. The model was used to predict the incidence of hepatitis C in the second half and the last quarter of 2017, and compared with the actual values. The prediction effects of the four models were compared and analyzed using the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). Results The SVR model performs not well. However, ARIMA model, NNAR model and stacked LSTM model can identify the incidence trend of hepatitis C in China from 2007 to 2017, and the RMSE values of the ARIMA model and the NNAR model are larger, and these two are relatively similar. On the contrary, the RMSE value of the stacked LSTM model is smaller. On the whole, compared with ARIMA model and NNAR model, it decreases by at least 20 percentage. The predicted MAPE value of the stacked LSTM model is less than 1%, meanwhile it is lower than the value of ARIMA or NNAR models. Conclusion The stacked LSTM model has the best predictive effect on the incidence of hepatitis C.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"145 30","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A keyword extraction method for Chinese professional field text based on improved RAKE 基于改进RAKE的中文专业领域文本关键字提取方法

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667258

Yuxin Xie, Jiang Yang, Tai-yong fei, Baochun Yu, Xin Hu

引用次数: 0

A service load interval prediction method for cloud-edge collaborations based on type identification and SVMs 基于类型识别和支持向量机的云边缘协作服务负荷区间预测方法

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667219

Yue Meng, Jiaxi Chen, Xingchuan Liu

{"title":"A service load interval prediction method for cloud-edge collaborations based on type identification and SVMs","authors":"Yue Meng, Jiaxi Chen, Xingchuan Liu","doi":"10.1117/12.2667219","DOIUrl":"https://doi.org/10.1117/12.2667219","url":null,"abstract":"Service load prediction is a critical basis of cloud-edge autonomous collaborations which mainly considers the rapid response of tasks and load balancing of multiple terminals. Traditional load forecasting is usually in the form of point estimation with a relatively high variance. Frequent changes in point estimation may lead to scheduling errors and waste of resources, thus is not suitable for application scenarios of cloud-edge collaborations. To solve these problems, this paper proposed a service load interval prediction method for cloud-edge collaborations based on type identification and SVMs. The main function of the proposed method is to provide the upper and lower bounds of load forecasting suitable for cloud side collaborations with stronger adaptability to load changes. It mainly includes four steps: service load type identification, load history data interval construction, parameter optimization of SVM, and load interval prediction. This paper takes three types of cloud-edge collaborative tasks in robot target tracking (visual location, target analysis, route planning) as examples, and carried out a large number of experiments to verify the effectiveness of this method. The result shows that it outperforms traditional methods in normalized interval proportion of average width and comprehensive width coverage to a great extent.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116564107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Household occupancy and energy consumption prediction for energy data big data mining 住户入住率与能耗预测的能源数据大数据挖掘

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667622

Hangdong An

引用次数: 0

Prediction model of disease and pest epidemic of jujube based on Internet of Things 基于物联网的枣树病虫害流行预测模型

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667739

Feng Liu

{"title":"Prediction model of disease and pest epidemic of jujube based on Internet of Things","authors":"Feng Liu","doi":"10.1117/12.2667739","DOIUrl":"https://doi.org/10.1117/12.2667739","url":null,"abstract":"To reduce the loss of the jujube production and develop more effective pest control measures, this paper studies the prediction model of jujube pest epidemic based on the Internet of Things technology. The data of jujube growth was collected based on the Internet of Things technology, and multi-source information was obtained by combining sensor technology and network communication technology. The characteristic bands of the jujube image were extracted. The useless bands were removed, and the overall physical information of the image was retained. The prediction model of the jujube pest and disease epidemic situation was established, and the prediction factors with poor stability were processed by BP neural network structure to realize the prediction. Through experimental demonstration and analysis, compared with the actual occurrence degree of diseases and insect pests, the prediction curve of the occurrence degree of diseases and insect pests of jujube by this model is consistent with the changing trend of the actual occurrence degree curve. Compared with the traditional model, the prediction error of the proposed model is smaller, which proves that the proposed model is more effective. In this paper, the model can obtain effective information on the basis of multi-source data, and get more accurate prediction results, which provides a scientific basis for decision-making for the formulation of pest and disease control plan to ensure the high and stable yield of jujube.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124453503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic environment monitoring system of digital twin computer room based on Drools inference engine 基于Drools推理机的数字孪生机房动态环境监测系统

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667222

Bin Dong, Yue Sun, Wei Chen, Xiaotian Xu, Yue Zhang

引用次数: 0

EEG feature extraction methods in motor imagery brain computer interface 运动图像脑机接口的脑电特征提取方法

Third International Seminar on Artificial Intelligence, Networking, and Information Technology Pub Date : 2023-02-22 DOI: 10.1117/12.2667875

Fengge Bao, Weiheng Liu

引用次数: 0